Identifying Relations for Open Information Extraction

  title={Identifying Relations for Open Information Extraction},
  author={Anthony Fader and Stephen Soderland and Oren Etzioni},
Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-ofthe-art Open IE systems is rife with uninformative and incoherent extractions. To overcome these problems, we introduce two simple syntactic and lexical constraints on binary relations expressed by verbs. We implemented the constraints in the REVERB Open IE system, which more than doubles the area under the precision… CONTINUE READING
Highly Influential
This paper has highly influenced 165 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 1,034 citations. REVIEW CITATIONS

From This Paper

Results and topics from this paper.

Key Quantitative Results

  • More than 30% of REVERB’s extractions are at precision 0.8 or higher— compared to virtually none for earlier systems.
664 Citations
32 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 664 extracted citations

1,035 Citations

Citations per Year
Semantic Scholar estimates that this publication has 1,035 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.

Similar Papers

Loading similar papers…